U.S. patent application number 10/791256 was filed with the patent office on 2004-09-02 for method and apparatus for multidomain data analysis.
Invention is credited to Sidorowich, John J..
Application Number | 20040172202 10/791256 |
Document ID | / |
Family ID | 24165017 |
Filed Date | 2004-09-02 |
United States Patent
Application |
20040172202 |
Kind Code |
A1 |
Sidorowich, John J. |
September 2, 2004 |
Method and apparatus for multidomain data analysis
Abstract
An optical measuring device generates a plurality of measured
optical data from inspection of a thin film stack. The measured
optical data group naturally into several domains. In turn the thin
film parameters associated with the data fall into two categories:
local and global. Local "genes" represent parameters that are
associated with only one domain, while global genes represent
parameters that are associated with multiple domains. A processor
evolves models for the data associated with each domain, which
models are compared to the measured data, and a "best fit" solution
is provided as the result. Each model of theoretical data is
represented by an underlying "genotype" which is an ordered set of
the genes. For each domain a "population" of genotypes is evolved
through the use of a genetic algorithm. The global genes are
allowed to "migrate" among multiple domains during the evolution
process. Each genotype has a fitness associated therewith based on
how much the theoretical data predicted by the genotype differs
from the measured data. During the evolution process, individual
genotypes are selected based on fitness, then a genetic operation
is performed on the selected genotypes to produce new genotypes.
Multiple generations of genotypes are evolved until an acceptable
solution is obtained or other termination criterion is
satisfied.
Inventors: |
Sidorowich, John J.; (Santa
Cruz, CA) |
Correspondence
Address: |
STALLMAN & POLLOCK LLP
SUITE 2200
353 SACRAMENTO STREET
SAN FRANCISCO
CA
94111
US
|
Family ID: |
24165017 |
Appl. No.: |
10/791256 |
Filed: |
March 2, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10791256 |
Mar 2, 2004 |
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10349262 |
Jan 22, 2003 |
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10349262 |
Jan 22, 2003 |
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09542724 |
Apr 4, 2000 |
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6532076 |
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Current U.S.
Class: |
702/40 |
Current CPC
Class: |
G06K 9/6229 20130101;
G01N 21/8422 20130101; G06N 3/126 20130101; G01N 21/9501
20130101 |
Class at
Publication: |
702/040 |
International
Class: |
A01H 005/00 |
Claims
We claim:
1. A multidomain method for evaluating parameters of a
semiconductor wafer or wafer set comprising: identifying a group of
semiconductor wafer parameters-to be evaluated; mapping the
semiconductor wafer parameters into at least one genotype, said
genotype comprising a collection of genes, each gene corresponding
to a selected one of the group of semiconductor wafer parameters to
be evaluated; defining more than one domain as a collection of
genotypes, each domain with its own population of genotypes;
deriving a set of theoretical data for each genotype; inspecting
the semiconductor wafer or wafer set using a beam of radiation and
generating therefrom sets of measured data; comparing a set of
measured data to a corresponding set of derived theoretical data
for each genotype in each domain in order to determine a level of
fitness for each genotype; migrating genotypes among the domains by
selecting at least one genotype from a current domain population
and adding it to the population of a different domain; evolving a
next population for each domain by selecting at least one genotype
from the current population based on the fitness level of the
genotype and performing a genetic operation on the at least one
genotype, thereby creating at least one new genotype and adding the
at least one new genotype to the next population; and repeating the
comparing, migrating, and evolving steps so that for each domain
the fittest genotype becomes increasingly more fit.
2. A method as recited in claim 1 wherein the likelihood of
selecting a genotype for a genetic operation from a domain
population is proportional to the degree to which the derived
theoretical data associated with that genotype matches the measured
data.
3. A method as recited in claim 1 wherein the likelihood of
selecting a genotype for migration from a domain population depends
upon the degree to which the derived theoretical data associated
with that genotype matches the measured data.
4. A method as recited in claim 1, wherein the level of fitness of
a genotype is determined by calculating the fitness as a function
of the difference between the theoretical data and the measured
data.
5. A method as recited in claim 1, wherein the performing step
includes reproducing an identical copy of the at least one
genotype.
6. A method as recited in claim 1, wherein the performing step
includes selecting a gene from the at least one genotype and
mutating the gene.
7. A method as recited in claim 1, wherein the performing step
includes selecting corresponding genes in a pair of genotypes and
exchanging the genes.
8. A method as recited in claim 6, wherein the step of selecting a
gene includes randomly selecting the gene.
9. A method as recited in claim 7, wherein the step of selecting
corresponding genes includes randomly selecting the genes.
10. A method as recited in claim 1, wherein the step of inspecting
includes measuring one wafer at multiple points with data generated
from each point being used to define a set of measured data.
11. A method as recited in claim 1, wherein the step of inspecting
includes measuring multiple wafers.
12. A multidomain method for evaluating parameters of a
semiconductor wafer or wafer set, comprising: identifying a group
of semiconductor wafer parameters to be evaluated; mapping the
semiconductor wafer parameters into at least one genotype, said
genotype comprising a collection of genes, each gene corresponding
to a selected one of the group of semiconductor wafer parameters to
be evaluated; defining more than one domain as a collection of
genotypes, each domain with its own population of genotypes;
deriving a set of theoretical data for each genotype; inspecting
the semiconductor wafer or wafer set using a beam of radiation and
generating therefrom sets of measured data; comparing a set of
measured data to a corresponding set of derived theoretical data
for each genotype in each domain in order to determine a level of
fitness for each genotype; migrating genotypes among the domains by
selecting at least one genotype from a current domain population
and adding it to the population of a different domain; evolving a
new population of genotypes for each domain by selecting at least
one genotype from the current domain population based on the
fitness of the genotype and performing a genetic operation on the
at least one genotype to form at least one new genotype, said
genetic operation being selected from one of the following:
reproducing an identical copy of the at least one genotype;
selecting a gene from the at least one genotype and mutating the
gene; or selecting corresponding genes in a pair of genotypes and
exchanging the genes, and adding the at least one new genotype to a
next population; and repeating the comparing, migrating, and
evolving steps so that for each domain the fittest genotype becomes
increasingly more fit.
13. A method as recited in claim 12, wherein the step of selecting
at least one genotype for evolution includes selecting at least one
genotype in proportion to its fitness.
14. A method as recited in claim 12 wherein the likelihood of
selecting a genotype for migration from a domain population depends
upon the degree to which the derived theoretical data associated
with that genotype matches the measured data.
15. A method as recited in claim 12, wherein the step of selecting
a gene includes randomly selecting the gene.
16. A method as recited in claim 12, wherein the step of selecting
corresponding genes includes randomly selecting the genes.
17. A multidomain method for evaluating parameters of a
semiconductor wafer or wafer set comprising: identifying a group of
semiconductor wafer parameters to be evaluated; mapping the
semiconductor wafer parameters into at least one genotype, said
genotype comprising a collection of genes, each gene corresponding
to a selected one of the group of semiconductor wafer parameters to
be evaluated; dividing the genes into at least two different gene
classes, at least one of said gene classes being subject to a
migration operation; defining more than one domain as a collection
of genotypes, each domain with its own population of genotypes;
deriving a set of theoretical data for each genotype; inspecting
the semiconductor wafer or wafer set using a beam of radiation and
generating therefrom sets of measured data; comparing a set of
measured data to a corresponding set of derived theoretical data
for each genotype in each domain in order to determine a level of
fitness for each genotype; applying a migration operation to at
least one genotype from a current domain population, said migration
operation including a mechanism for moving genes in at least one of
said gene classes to the genotype population of a different domain;
evolving a next population for each domain by selecting at least
one genotype from the current population based on the fitness level
of the genotype and performing a genetic operation on the at least
one genotype, thereby creating at least one new genotype and adding
the at least one new genotype to the next population; and repeating
the comparing, migration operation, and evolving steps so that for
each domain the fittest genotype becomes increasingly more fit.
18. A method as recited in claim 17 wherein the likelihood of
selecting a genotype for a genetic operation from a domain
population is proportional to the degree to which the derived
theoretical data associated with that genotype matches the measured
data.
19. A method as recited in claim 17 wherein the likelihood of
selecting a genotype for migration from a domain population depends
upon the degree to which the derived theoretical data associated
with that genotype matches the measured data.
20. A method as recited in claim 17, wherein the level of fitness
of a genotype is determined by calculating the fitness as a
function of the difference between the theoretical data and the
measured data.
21. A method as recited in claim 17, wherein the performing step
includes reproducing an identical copy of the at least one
genotype.
22. A method as recited in claim 17, wherein the performing step
includes selecting a gene from the at least one genotype and
mutating the gene.
23. A method as recited in claim 17, wherein the performing step
includes selecting corresponding genes in a pair of genotypes and
exchanging the genes.
24. A method as recited in claim 22, wherein the step of selecting
a gene includes randomly selecting the gene.
25. A method as recited in claim 23, wherein the step of selecting
corresponding genes includes randomly selecting the genes.
26. A method as recited in claim 17, wherein the permissible range
of values for genes in at least one of said gene classes that is
subject to a migration operation becomes increasingly narrow as the
comparing, migration operation, and evolving steps are
repeated.
27. A multidomain method for evaluating parameters of a
semiconductor wafer or wafer set, comprising: identifying a group
of semiconductor wafer parameters to be evaluated; mapping the
semiconductor wafer parameters into at least one genotype, said
genotype comprising a collection of genes, each gene corresponding
to a selected one of the group of semiconductor wafer parameters to
be evaluated; dividing the genes into at least two different gene
classes, at least one of said gene classes being subject to a
migration operation; defining more than one domain as a collection
of genotypes, each domain with its own population of genotypes;
deriving a set of theoretical data for each genotype; inspecting
the semiconductor wafer or wafer set using a beam of radiation and
generating therefrom sets of measured data; comparing a set of
measured data to a corresponding set of derived theoretical data
for each genotype in each domain in order to determine a level of
fitness for each genotype; applying a migration operation to at
least one genotype from a current domain population, said migration
operation including a mechanism for moving genes in at least one of
said gene classes to the genotype population of a different domain;
evolving a new population of genotypes for each domain by selecting
at least one genotype from the current domain population based on
the fitness of the genotype and performing a genetic operation on
the at least one genotype to form at least one new genotype, said
genetic operation being selected from one of the following:
reproducing an identical copy of the at least one genotype;
selecting a gene from the at least one genotype and mutating the
gene; or selecting corresponding genes in a pair of genotypes and
exchanging the genes, and adding the at least one new genotype to a
next population; and repeating the comparing, migration operation,
and evolving steps so that for each domain the fittest genotype
becomes increasingly more fit.
28. A method as recited in claim 27, wherein the step of selecting
at least one genotype for evolution includes selecting at least one
genotype in proportion to its fitness.
29. A method as recited in claim 27 wherein the likelihood of
selecting a genotype for migration from a domain population depends
upon the degree to which the derived theoretical data associated
with that genotype matches the measured data.
30. A method as recited in claim 27, wherein the step of selecting
a gene includes randomly selecting the gene.
31. A method as recited in claim 27, wherein the step of selecting
corresponding genes includes randomly selecting the genes.
32. A method as recited in claim 27, wherein the permissible range
of values for genes in at least one of said gene classes that is
subject to a migration operation becomes increasingly narrow as the
comparing, migration operation, and evolving steps are
repeated.
33. A method as recited in claim 1 or claim 12 or claim 17 or claim
27 wherein the step of deriving a set of theoretical data for each
genotype includes the use of a nonlinear least squares optimization
algorithm for selected genotypes.
34. A multidomain process for evaluating parameters of a
semiconductor wafer or wafer set comprising: identifying a group of
semiconductor wafer parameters to be evaluated; inspecting the
semiconductor wafer or wafer set using a beam of radiation and
generating therefrom sets of measured data, each set of measured
data corresponding to a different measurement; defining more than
one search domain, each search domain corresponding to a set of
measured data; applying an iterative search method to each search
domain in order to generate a group of optimized parameter values
for each search domain, said iterative search method including the
steps of: generating a set of theoretical parameter values
associated with each search domain; deriving a set of theoretical
data for each set of theoretical parameter values; comparing the
sets of theoretical data associated with the set of parameter
values associated with each search domain to the set of measured
data corresponding to each search domain; generating a new set of
theoretical parameter values of each search domain based on the
comparing step in a manner so as to identify increasingly more
optimal theoretical parameter values for each search domain; and
wherein said iterative search method further includes the step of
migrating a set of theoretical parameter values from one search
domain to another search domain.
35. A method as recited in claim 34, wherein the step of inspecting
includes measuring one wafer at multiple points.
36. A method as recited in claim 34, wherein the step of inspecting
includes measuring multiple wafers.
37. A method as recited in claim 34 wherein said iterative search
method is a genetic algorithm.
Description
FIELD OF THE INVENTION
[0001] This invention relates to a multidomain method for
evaluating the formation of thin films on semiconductor substrates
using optical methods, and an apparatus embodying the method.
BACKGROUND OF THE INVENTION
[0002] Optical methods for measuring samples are generally known,
in particular, for semiconductor fabrication involving the
formation of a stack of thin film layers on a semiconductor
substrate. Such methods are considered essential for the efficient
operation of modern fabrication facilities. Optical methods are
desirable because they are non-destructive and the resultant
optical data can be used to derive information regarding layer
parameters, such as thickness, refractive index, extinction
coefficient, dispersion and scattering, for multiple layers of a
thin film stack.
[0003] One preferred approach includes the use of the OPTIPROBE
detector manufactured and sold by Therma-Wave, Inc. of Fremont,
Calif., assignee herein, and described in part in one or more of
the following U.S. Pat. Nos. 4,999,014; 5,042,951; 5,181,080;
5,412,473; and PCT publication WO 99/02970, each of which is
incorporated herein by reference in its entirety.
[0004] Conventional optical processing technology typically relies
upon using a non-linear least squares algorithm to fit the measured
data to a set of data points with a solution representing specific
parameters of a thin film stack.
[0005] Improvements in optical technologies can provide an
ever-increasing number of measured data points, which in turn
provide the opportunity for deriving layer parameters on more
complicated film stacks. However, this opportunity also presents a
more complex optimization problem for developing solutions based on
the observed data, and conventional processing techniques (such as
least squares algorithms) are inadequate to handle the increased
complexity.
[0006] Genetic Algorithms (GA's) have been applied to the problem
of adaptive function optimization. A basic theoretical framework
for GA's is described in Holland, Adaptation in Natural and
Artificial Systems (1975). The terminology used by Holland is
borrowed from genetics. Thus, in the computer analog, a GA is a
method for defining a "population" of solutions to a selected
problem, then evolving new populations by using probabilistic
genetic operations to act on "individual" members of the
population, i.e. individual solutions. Each individual in the
population has a plurality of "genes," which are each
representative of some real parameter of interest. For example, if
there are x data parameters of interest, each individual would have
x genes, and populations of individuals having x genes would be
propagated by a GA.
[0007] The use of GA's for function optimization is generally
described in U.S. Pat. No. 5,222,192 and U.S. Pat. No. 5,255,345,
both to Schaefer. Further, U.S. Pat. No. 5,394,509 to Winston
generally describes the application of GA's to search for improved
results from a manufacturing process. Also, there has recently been
much interest in the use of GA's in the design of various types of
optical filters. See Eisenhammer, et al., Optimization of
Interference Filters with Genetic Algorithms Applied to
Silver-Based Heat Mirrors, Applied Optics, Vol. 32 at pp. 6310-15
(1993); and Bck & Schutz, Evolution Strategies for
Mixed-Integer Optimization of Optical Multilayer Systems,
Proceedings of the Fourth Annual Conference on Evolutionary
Programming at pp. 33-51 (1995).
[0008] More recently, GA's have been applied to the problem of
evaluating thin films on semiconductor wafers. U.S. Pat. No.
5,864,633, hereby incorporated by reference in its entirety,
describes the application of GA's to the problem of evaluating the
characteristics of thin film layers with an optical inspection
device. The present invention is directed to an improvement on the
method disclosed in U.S. Pat. No. 5,864,633 involving a multidomain
optimization technique.
SUMMARY OF THE INVENTION
[0009] The technique described in U.S. Pat. No. 5,864,633 relates
to an invention useful for converting optical measurements at a
point on a semiconductor wafer into a description of the thin films
beneath that point on the wafer. This application describes a
modification of the GA technique described in U.S. Pat. No.
5,864,633 in order to improve either the evaluation of wafer
measurements at multiple points on a wafer or the evaluation of
(possibly multiple) measurements of multiple wafers.
[0010] The present invention provides a suitable multidomain
optimization technique for doing this wherein two or more
populations of genotypes are used. In general one evolving genotype
population is employed for each of the individual measurement
points on the wafer. Flags are used to divide the genes of the
genotypes into two categories or classes: local and global. Local
genes represent parameters that are only associated with one domain
whereas global genes represent parameters associated with more than
one domain. More than one category of global gene may be
employed.
[0011] By subdividing the genes into global and local categories
and employing a migration step that allows genotypes to move among
two or more domains, the present invention improves the evaluation
of the sample compared with the method taught in U.S. Pat. No.
5,864,633. This is so because the present invention allows the
optimization process to reflect the possibility that some
parameters may be constant or nearly constant across multiple
domains whereas others may not be.
[0012] Multiple generations of the genotypes in each domain are
evolved until an acceptable solution is obtained. Using
conventional Fresnel equations, a processor derives theoretical
data from the theoretical parameters defining each of the
genotypes. The derived theoretical data for a given genotype are
compared with the actual measured data in accordance with a fitness
function. The fitness function provides a measure of how close the
derived theoretical data are to the measured data. Individual
genotypes are then selected based on this fitness comparison.
Genetic operations are performed on the selected genotypes to
produce new genotypes. In addition to crossover, direct
reproduction, and mutation operations, a migration step is
performed that allows genotypes to migrate among two or more
domains.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a block diagram illustrating a simplified optical
inspection system.
[0014] FIG. 2 shows a thin film sample on which a linescan of
measurements has been made at 10 different points.
[0015] FIG. 3 shows a flow chart illustrating a portion of a method
according to present invention.
[0016] FIG. 4 shows a flow chart illustrating an additional portion
of the method shown in FIG. 3 according to present invention.
[0017] FIGS. 5A through 5C are flow chart portions illustrating the
use of different genetic operations.
[0018] FIG. 6 shows a flow chart illustrating an alternative method
according to the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0019] FIG. 1 illustrates a block diagram for a basic optical
inspection system 20 for measuring optical characteristics of a
sample 28, such as a semiconductor wafer having one or more thin
film layers 32 formed thereon. A light source 22 generates a probe
beam of light 24 which is reflected by beam splitter 26 through
lens 30 onto the sample 28. The probe beam of light 24 is reflected
off the sample 28 back through lens 30 and beam splitter 26 onto a
photodetector 50. Photodetector 50 generates a plurality of outputs
51 which are supplied to a processor 52. The outputs are used to
evaluate physical characteristics of the sample, and more
particularly, of the thin film layer(s) 32 of the sample.
[0020] It will be appreciated by those skilled in the art that many
configurations for an optical inspection system are possible, such
as those described in U.S. Pat. No. 4,999,014; U.S. Pat. No.
5,042,951; U.S. Pat. No. 5,181,080; U.S. Pat. No. 5,412,473; and
PCT publication WO 99/02970, each of which is incorporated herein
by reference in its entirety. The preferred optical inspection
system employs the OPTIPROBE detector manufactured and sold by
Therma-Wave, Inc. of Fremont, Calif. These patents describe how
measurements may be taken at multiple wavelengths and at multiple
angles of incidence either simultaneously or serially. However, for
the purpose of the present invention, it is sufficient to have an
optical inspection system which generates multiple optical data
measurements from the inspection of the semiconductor wafer.
[0021] An inspection system based on X-rays could also be used to
generate the data measurements. A preferred X-ray inspection system
is described in U.S. Pat. No. 5,619,548, issued Apr. 8, 1997, which
is hereby incorporated by reference in its entirety.
[0022] The optical data measurements will typically take the form
of amplitude information, such as reflectance versus angle of
incidence, or reflectance versus-wavelength, or polarization phase
information, such as provided by ellipsometry. For example, the
OPTIPROBE detector uses each of these techniques to take a large
number of measurements in a single scan, then it filters the
measured data resulting in from tens to hundreds of data points for
each set of measurements.
[0023] Well known Fresnel equations can be used to predict or model
the optical measurements expected from a known stack of layers with
specified thicknesses, reflection indices and extinction
coefficients. See Born & Wolf, Principles of Optics. However,
the nature of the Fresnel equations is such that they cannot be
easily inverted in order to unambiguously determine the various
parameters of a thin film stack from a large number of multiple
measured data points.
[0024] For this reason it is not easy to associate a correct set of
theoretical parameters with measured data. Given a set of
"theoretical" parameters which might correspond to the actual
parameters of the stack to be evaluated, one can program a
processor, using the Fresnel equations, to derive a set of
theoretical data based on these theoretical parameters. The derived
theoretical data may then be compared to the measured data and if
there is a reasonable level of correspondence, one can assume that
the generated theoretical parameters fairly describe the parameters
of the thin film stack under investigation.
[0025] Of course, it would be highly unlikely that the first set of
generated theoretical parameters, and the associated derived
theoretical data, would provide a good match to the actual measured
data. In the practice of the invention, the processor will generate
thousands of sets of theoretical parameters. In accordance with the
subject invention, the steps of generating the multiple sets of
theoretical parameters are performed using a genetic algorithm.
[0026] FIG. 2 illustrates an example of the type of problem that
might be addressed by the present invention. FIG. 2 shows a sample
with a dielectric thin film layer 80 on a substrate 60. The
thickness of the thin film layer 80 increases in a continuous but
unknown manner along the length of the sample. By way of example,
one may wish to determine the thicknesses T.sub.i and indices of
refraction N.sub.i of the thin film layer at multiple points on the
sample rather than a single point. Given a linescan measurement
that provides data at ten different points on the wafer, as shown
in FIG. 2, it is desirable to ultimately parameterize the thin film
thickness estimates on a point by point basis (10 parameters), but
yet to ultimately parameterize a single value for the thin film
index of refraction over all ten points (1 parameter).
[0027] According to the present invention, one optimizes the design
of the genotypes to reflect the multidomain nature of the problem
that one is addressing.
[0028] In the case of the FIG. 2 example, one would employ 10
domains or genotype populations, one corresponding to each point
for which data was collected during the linescan. A genotype may be
defined as an ordered set of genes, each gene representing a
different thin film parameter of interest. In this example, each
genotype would consist of an ordered pair of values T.sub.i and
N.sub.i, representing the thickness and index of refraction of the
thin film layer at a given point, respectively. The initial
population of genotypes for each domain can be varied based on the
complexity of the problem and the amount of computation time and
power available. By way of illustration, an initial population of
100 genotypes might be chosen for each of the 10 domains in the
FIG. 2 example.
[0029] In accordance with the present invention, one classifies the
individual genes as local or global. More than two categories of
genes could also be employed. During the evolutionary GA process
the global genes will be allowed to migrate among two or more
domains. In the FIG. 2 problem, for example, one would classify the
genes representing thickness as local and the genes representing
index of refraction as global, since it may be assumed that the
index is relatively constant. Typically the selection of genotypes
for migration would be based partly on the fitness of the genotypes
in which they occur and partly on chance. In other words, while
random, the selection process would typically be weighted according
to gentotype fitness. Migration of genotypes from a given domain
associated with a given measurement point on the sample could be
allowed to all other 9 domains or could be limited to domains
associated with "neighboring" measurement points. Typically migrant
genotypes received in a new domain would replace the least fit
genotypes in the new domain. Alternatively, rather than migrating
entire selected genotypes, the global genes from selected genotypes
in a given domain could be used to replace corresponding global
genes in selected genotypes in a new domain. In this alternative,
then, global genes themselves, rather than entire genotypes, would
in effect "migrate" by replacing the corresponding genes in
selected genotypes in a new domain.
[0030] The GA process may be allowed to evolve until some
termination criterion is satisfied. Such a termination criterion
could involve the setting of a maximum number of generations, or a
test for relative stability among the most fit, "elite" genotypes,
or both. One expects that, as the domains evolve under the GA, the
global genes of the elite genotypes in the different domains will
tend to converge in value. In the case of the FIG. 2 problem, one
would expect that at the end of the GA process the range of values
for the global genes of the elite genotypes of the 10 domains would
be much narrower than the range of values for the local genes of
the elite genotypes. This reflects the physical fact that along the
sample there is less variance in the index of refraction in the
FIG. 2 thin film layer 80 than in its thickness.
[0031] Although the present invention is described in the context
of genetic search algorithms, it could just also be applied to
other kinds of iterative search algorithms (e.g., a neural network)
that might be used to solve the multidomain problem of finding
optimal parameter values for a semiconductor wafer sample. Given
optical measurements made at different data points on a
semiconductor wafer or wafer set, as long as some of the parameters
are local in nature so that they should vary significantly between
the data points, one would want to improve the search process by
defining multiple search domains corresponding to the different
measurement data points. And as long as some of the parameters are
global in nature so that they should vary little between the data
points, one would want to apply some type of cross-talk operation
to improve the search process by communicating information about
the global parameters among the different search domains as the
search process proceeds.
[0032] Referring now to FIG. 3, a flow chart illustrates one
method, which may be implemented in processor 52 with suitable
programming, to carry out the present invention. It should be
recognized that many variations in methodology could be used
without affecting the scope of this invention. Processor 52 can be
any general purpose computer having adequate computing resources
for performing iterative processing. For example, a PENTIUM III
processor running the LINUX operating system could be used.
[0033] In step 98, the set of thin film parameters to be optimized
are chosen and mapped into a genotype. In practical terms, each
parameter of interest or gene can be mapped into an individual data
store, its range specified, and its contents supplied, altered, or
otherwise operated on in accordance with suitable programming. The
collection of individual data stores which store all the parameters
of interest for a given measurement constitutes an individual
genotype. It can be appreciated that the genotypes may be handled
with common data processing commands to operate on the information
stored therein for any suitable purpose. The present invention
employs a GA to operate on selected genes to propagate additional
genotypes having generally increasing fitness.
[0034] In addition in step 98 the genes are classified into local
and global categories. As described above, global genes will be
allowed to migrate among two or more domains. More than two
categories of genes may be employed with the different categories
of genes having different levels of migration privileges.
[0035] In step 99, a group of more than one initial populations,
each comprising M.sub.p individual genotypes is created either at
random or by arbitrary means. For example, the initial p
populations may be initialized with preexisting data from prior
measurements. The number of initial genotypes M.sub.p "seeded" into
each of the p genotype populations is also arbitrary and may be
chosen in light of the computer power available and the complexity
of the problem.
[0036] The fitness of each genotype in the current populations is
evaluated and stored for reference in step 100. The fitness is
determined by a fitness function F, which is based on the
parameters of interest. The fitness F may be defined as a function
of the residual value between a measured data point x.sub.i and a
theoretical data point y.sub.i, for N measurements, for
example:
F=(RES), e.g.
2-{square root}{square root over (RES)}
[0037] where 1 RES = ( 1 / N ) i = 1 N ( x i - y i ) 2
[0038] One way in which the fitness F may be measured is to apply
the Fresnel equations to the parameters of a genotype to get
predicted values that can then be compared with the measurement
values. Another way to measure fitness is to take the parameters of
the genotypes as the starting point for performing an iterative
nonlinear least squares optimization technique such as the
well-known Marquardt-Levenberg algorithm. Fitness is then measured
by comparing the values predicted by the results of the nonlinear
least squares optimization technique to the measurement values. A
suitable iterative optimization technique for this purpose is
described in "Multiparameter Measurements of Thin Films Using
Beam-Profile Reflectivity," Fanton et al., Journal of Applied
Physics, Vol. 73, No. 11. p. 7035 (1993) and "Simultaneous
Measurement of Six Layers in a Silicon on Insulator Film Stack
Using Spectrophotometry and Beam Profile Reflectometry," Leng et
al., Journal of Applied Physics, Vol. 81, No. 8, p. 3570 (1997).
These two articles are hereby incorporated by reference in their
entireties. Fitness could also be measured using the Fresnel
equations directly sometimes and using a nonlinear optimization
technique at other times.
[0039] The variable GEN is used to identify the generation number
and is then initialized to zero.
[0040] In step 101, termination criteria are examined, and if the
criteria are satisfied, a preferred solution results in the
methodology of FIG. 4 being applied ("point D").
[0041] The selection of genotypes in step 102 is statistically
based upon how closely the theoretical data associated with the
genotypes fits with the measured data. The selection is such that
it is more likely that a genotype having a high fitness will be
selected than one having a low fitness. This type of selection
process is sometimes called a weighted lottery. In the preferred
embodiment, a newly migrated genotype must reside in the new
population for at least one generation before it can be selected
again for migration via step 102.
[0042] Step 103 moves the selected, migrant genotype to another
population domain. The population that receives the migrant
genotype may be selected at random or may be restricted to some
subset such as the "nearest neighbor" populations. The genotype
with the worst fitness in the selected population domain is
replaced with the migrant genotype, so long as the replaced
genotype is not a recent migrant. If the genotype with the worst
fitness is a recent migrant, then the genotype with the worst
fitness among those genotypes that are not recent migrants may be
used for replacement. In an alternative embodiment, rather than
migrating entire selected genotypes, the global genes from selected
genotypes in a given domain could be used to replace the global
genes in genotypes in a new domain. In this alternative, then,
global genes themselves, rather than entire genotypes, would in
effect migrate.
[0043] In step 104 the fitness of the newly received migrants in
each domain is evaluated in the same manner as was done in step
100.
[0044] In step 105, counters for each domain, such as counter i,
are reset to zero. Counter i counts the number of genotypes which
are created in the next generation population. In this part of the
routine a new population of genotypes is propagated in each
population domain through genetic operations and forms the next
generation. In order to count the number of genotypes in the
population domain, a counter is initialized for each population
domain and thereafter serves to track the number of genotypes which
are genetically propagated in the bottom portion of the
routine.
[0045] In step 106, the counters are compared to the preset values
M.sub.p. Once all the counters for the populations p are equal to
the corresponding M.sub.p values, then the new populations are full
and the generation number GEN is incremented by one in step 114.
The routine then returns to step 101 to either terminate or begin
constructing another generation of genotypes. If the new
populations are not full, the routine proceeds to step 108 and
evolves one or more new genotypes for the new generation.
[0046] In step 108, a genetic operation is selected. The selection
among genetic operations will usually be made probabilistically,
but could be performed arbitrarily. There are three basic genetic
operations, namely direct reproduction, crossover and mutation, as
illustrated in FIGS. 5A, 5B, and 5C, although the invention is not
strictly limited in this sense. Each of these genetic operations
should be employed to some degree to provide a sufficiently random
evolution of the genotypes, although this is not strictly
required.
[0047] For each of the three possible genetic operations, either
one or two genotypes are selected from the current population. The
genotype selected in step 109 is statistically based upon how
closely the theoretical data associated with that genotype "fits"
with the measured data. Although the selection is by chance, it is
more likely that a genotype having a high fitness F will be
selected than one having a low fitness. In the preferred
embodiment, the likelihood of being selected is directly
proportional to the fitness. By selecting the genotypes in this
Darwinian fashion, the population can evolve in a manner so that
the genotypes migrate towards progressively better fitting
solutions. In addition, by using a weighted, but still random
selection process, it is possible to search for best fit solutions
over the entire population. This provides a more efficient search
of the total solution space than can be achieved using nonlinear
least square fitting algorithms that use search strategies that by
their nature are much more localized.
[0048] The chosen form of genetic operation will be carried out in
step 110. The new genotype(s) created by the genetic operation are
then written into the new population in step 111, and the counter
for the respective population domain is incremented in step
112.
[0049] Steps 108 through 112 may be carried out in many different
ways without departing from the scope of the invention. For
example, the three basic genetic operations are illustrated in
FIGS. 5A-5C. If direct reproduction is chosen in step 108a, a
single genotype is selected in step 109a. As noted above, this
selection is random, but weighted based on fitness. In step 110a,
an exact copy of that selected genotype is copied and inserted into
a new population (step 111a). The individual counter i for the
respective population domain p is then incremented in step 112a and
the routine returns to step 106 to propagate more genotypes until
the new population is full. Alternatively, the exact copy of the
selected genotype may be subjected to genetic mutation before being
copied into the new population, as indicated by the dotted line
connection B to step 109c.
[0050] If crossover is selected in step 108b, then two genotypes
are randomly chosen from the current population (step 109b) based
on their fitness. Crossover is then carried out in step 110b,
meaning that genes from each of the selected genotypes are selected
and exchanged, thereby forming two new genotypes which are then
written into the new population in step 111b. If crossover is
selected, the individual counter i for the respective population
domain must be incremented twice in step 112b since two new
genotypes are evolved. The routine returns to step 106 to propagate
more genotypes until the new population is full. Alternatively, the
crossover genotypes may be subjected to genetic mutation before
being copied into the new population, as indicated by the dotted
line connection B to step 109c.
[0051] If mutation is selected, then one genotype is chosen from
the current population in step 109c based on its fitness. Some
number of genes from the selected genotype are selected and then
mutated in step 110c, and the new genotype is written into the new
population in step 111c. The individual counter i is then
incremented in step 112 and the routine returns to step 106 to
propagate more genotypes until the new population is full at which
point the generation counter GEN is incremented.
[0052] As noted above, the selection of genotypes for use in the
genetic operation is generally according to a weighted lottery
based on fitness, although it is possible to force a selection
through direct intervention. Also, the selection of individual
genes to be operated upon is generally random.
[0053] As previously discussed, the routine will run until a
termination criterion is satisfied in step 101. In practice, the
termination criterion is designed to allow the population to evolve
for a predetermined number of generations. In this case, such a
predetermined number can be selected based on how fast the
processor runs and how long the operator is willing to wait for a
result. It should be understood that the longer the populations are
allowed to evolve, the more likely it is that a good fit will be
obtained. Other termination criteria could be established, such as
when the fitness of the best genotype of the population does not
improve by at least some selected amount .delta. over the last Q
generations. When the termination criteria is satisfied, another
routine shown in FIG. 4 is applied ("point D" of FIG. 3).
[0054] The routine that begins at point D can be described as
follows. In loops 300 and 400, one takes the best genotype from
each population domain and finds the average of each global
parameter. In other words, for each global parameter one sums the
corresponding gene values of the best genotypes from each domain in
loop 300 and then divides by the number of domains ("N") to yield a
global average in loop 400. For the best genotype in each domain,
the gene values corresponding to each global parameter are then
reset to this global average also in loop 400. Then in loop 500 one
takes the best genotype for each domain and performs a nonlinear
least squares optimization for the genes in that genotype that
correspond to the local parameters. At the end of this process, for
each domain, one has a solution genotype whose global parameter
genes are an average of those for the best genotypes of all of the
domains and whose local parameter genes are the result of the
nonlinear optimization process. As described above, a suitable
iterative optimization technique for this purpose is disclosed in
"Multiparameter Measurements of Thin Films Using Beam-Profile
Reflectivity," Fanton et al., Journal of Applied Physics, Vol. 73,
No. 11. p. 7035 (1993) and "Simultaneous Measurement of Six Layers
in a Silicon on Insulator Film Stack Using Spectrophotometry and
Beam Profile Reflectometry," Leng et al., Journal of Applied
Physics, Vol. 81, No. 8, p. 3570 (1997), which are incorporated by
reference.
[0055] An alternative to the method shown in loop 500 of FIG. 4
would be to again run a genetic algorithm in each domain during
which algorithm the global genes would be held fixed and only the
local genes would be allowed to evolve.
[0056] One expects that, as the domains evolve under the GA, the
global genes of the elite genotypes in the different domains will
tend to converge in value. For each global parameter, one could
test the associated global genes for such a convergence at each
generation and then limit the allowed range of values for those
global genes based on the degree of convergence obtained thus far.
This could be done simply by resetting gene values falling above
the allowed range to the range's maximum value, and gene values
falling below the allowed range to the range's minimum value. In
other words the allowed "search range" for the global genes may be
dynamically narrowed as the GA proceeds. This alternative
embodiment is shown in FIG. 6 in which the added step 113 prior to
the incrementing of the GEN counter represents such a dynamic
narrowing of the search range for the global genes as the GA
proceeds. After termination of the GA, as indicated by point D',
one may optionally proceed to optimize the local genes of the best
genotypes in each domain with a nonlinear least squares algorithm
as is shown in loop 500 of FIG. 4.
[0057] It should be understood that the invention is not intended
to be limited by the specifics of the above-described embodiment,
but rather defined by the accompanying claims.
* * * * *